# Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck

arXiv: https://arxiv.org/abs/2106.01177 · HTML: https://mlsanity.com/paper/2106.01177/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [Training Neural Networks with Local Error Signals](https://mlsanity.com/paper/1901.06656/) (2019)
- [Variational inference for Monte Carlo objectives](https://mlsanity.com/paper/1602.06725/) (2016)
- [Neuromorphic Deep Learning Machines](https://mlsanity.com/paper/1612.05596/) (2016)
- [An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications](https://mlsanity.com/paper/1910.01059/) (2019)
- [A Brief Introduction to Machine Learning for Engineers](https://mlsanity.com/paper/1709.02840/) (2017)
- [Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence](https://mlsanity.com/paper/1910.09594/) (2019)
- [VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits](https://mlsanity.com/paper/2004.09416/) (2020)
- [Spike Event Based Learning in Neural Networks](https://mlsanity.com/paper/1502.05777/) (2015)
- [Training Probabilistic Spiking Neural Networks with First-to-spike Decoding](https://mlsanity.com/paper/1710.10704/) (2017)
- [Naturalizing Neuromorphic Vision Event Streams Using GANs](https://mlsanity.com/paper/2102.07243/) (2021)

## Built on by (most cited first, 15 of 15)

- [Fully Spiking Variational Autoencoder](https://mlsanity.com/paper/2110.00375/) (2021)
- [Spiking Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.17046/) (2023)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Bayesian Continual Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.13723/) (2022)
- [Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning](https://mlsanity.com/paper/2111.01750/) (2021)
- [A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices](https://mlsanity.com/paper/2303.10780/) (2023)
- [Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks](https://mlsanity.com/paper/2308.10187/) (2023)
- [Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules](https://mlsanity.com/paper/2206.00823/) (2022)
- [Spiking Neural Network Decision Feedback Equalization](https://mlsanity.com/paper/2211.04756/) (2022)
- [Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer](https://mlsanity.com/paper/2211.16607/) (2022)
- [Spiking Diffusion Models](https://mlsanity.com/paper/2408.16467/) (2024)
- [ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling](https://mlsanity.com/paper/2310.14839/) (2023)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning](https://mlsanity.com/paper/2211.01952/) (2022)
- [Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck](https://mlsanity.com/paper/2404.01804/) (2024)

## Abstract (arXiv)

One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or images for generative models, and need to be encoded into spikes. This is done by handcrafting target spiking signals, which in turn implicitly fixes the mechanisms used to decode spikes into natural signals, e.g., rate decoding. The arbitrary choice of target signals and decoding rule generally impairs the capacity of the SNN to encode and process information in the timing of spikes. To address this problem, this work introduces a hybrid variational autoencoder architecture, consisting of an encoding SNN and a decoding Artificial Neural Network (ANN). The role of the decoding ANN is to learn how to best convert the spiking signals output by the SNN into the target natural signal. A novel end-to-end learning rule is introduced that optimizes a directed information bottleneck training criterion via surrogate gradients. We demonstrate the applicability of the technique in an experimental settings on various tasks, including real-life datasets.